File size: 3,733 Bytes
c69c125 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | from dataclasses import dataclass
from typing import Literal
import torch
from einops import rearrange, repeat
from jaxtyping import Float, Int
from torch import Tensor
from ...scene_trainer.gaussian_module import GaussiansModule
from ..types import Gaussians
from .cuda_splatting_fastgs import (
render_cuda_fastgs,
render_depth_cuda_fastgs,
render_metric_counts_fastgs,
)
from .splatting_cuda_decoder import SplattingCUDADecoder
@dataclass
class FastGSDecoderSplattingCUDACfg:
name: Literal["fastgs"]
scale_invariant: bool
# False: pass scales+rotations and let the CUDA kernel compute the covariance.
# True: precompute Python-side and pass cov3D_precomp.
use_covariances: bool = False
# FastGS compact-box multiplier (controls the tile count per splat); matches the
# FastGS training default.
mult: float = 0.5
class FastGSDecoderSplattingCUDA(SplattingCUDADecoder[FastGSDecoderSplattingCUDACfg]):
"""FastGS diff_gaussian_rasterization_fastgs backend. Only the rasterizer calls differ from
the shared base; see splatting_cuda_decoder.SplattingCUDADecoder for the orchestration."""
def _produces_abs_grad(self) -> bool:
# FastGS's [N,4] screen tensor carries the Abs-GS split signal in cols [2:]; expose it.
return True
def _raster(self, ext, intr, near, far, image_shape, bg, means, covars, shs, opacities,
scales, rotations_wxyz, means2d_out, means2d_abs_out=None):
return render_cuda_fastgs(
ext, intr, near, far, image_shape, bg, means, covars, shs, opacities,
scale_invariant=self.cfg.scale_invariant,
gaussian_scales=scales,
gaussian_rotations=rotations_wxyz,
mult=self.cfg.mult,
means2d_out=means2d_out,
means2d_abs_out=means2d_abs_out,
)
def _raster_depth(self, ext, intr, near, far, image_shape, means, covars, opacities, mode):
return render_depth_cuda_fastgs(
ext, intr, near, far, image_shape, means, covars, opacities,
mode=mode, scale_invariant=self.cfg.scale_invariant, mult=self.cfg.mult,
)
@torch.no_grad()
def render_metric_counts(
self,
gaussians: Gaussians | GaussiansModule,
extrinsics: Float[Tensor, "batch view 4 4"],
intrinsics: Float[Tensor, "batch view 3 3"],
near: Float[Tensor, "batch view"],
far: Float[Tensor, "batch view"],
image_shape: tuple[int, int],
metric_maps: Int[Tensor, "view height_width"], # per-view binary high-error pixel maps
) -> Int[Tensor, "view gaussian"]:
"""FastGS multi-view importance signal: per-view per-Gaussian counts of flagged (high-error)
pixels each Gaussian contributed to. Assumes batch size 1; ``metric_maps`` has one flattened
[H*W] map per view. Reduce with ``adc.fastgs.compute_fastgs_scores``."""
b, v, _, _ = extrinsics.shape
assert b == 1, "render_metric_counts assumes scene batch size 1"
means, shs, opacities, scales, rotations_wxyz, covars = self._prepare_flat_gaussians(gaussians, b, v)
bg = repeat(self.background_color, "c -> (b v) c", b=b, v=v)
return render_metric_counts_fastgs(
rearrange(extrinsics, "b v i j -> (b v) i j"),
rearrange(intrinsics, "b v i j -> (b v) i j"),
rearrange(near, "b v -> (b v)"),
rearrange(far, "b v -> (b v)"),
image_shape, bg, means,
covars,
shs, opacities, metric_maps,
scale_invariant=self.cfg.scale_invariant,
gaussian_scales=scales,
gaussian_rotations=rotations_wxyz,
mult=self.cfg.mult,
)
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